Understanding Disease Causation: Models, Triads, and Theories

Disease causation has puzzled humanity for centuries. Why do some people fall ill while others exposed to the same conditions remain healthy? The answer is far more complex than a single germ or a single risk factor. Over time, scientists and epidemiologists have developed several models to explain how diseases originate, spread, and progress. These models – from simple triads to intricate webs – form the foundation of modern public health strategy, helping us prevent disease, plan interventions, and allocate resources effectively.

Historical views on disease causes 🔗

For most of human history, disease was attributed to supernatural forces, divine punishment, or mystical imbalances. In ancient Greece, physicians like Hippocrates proposed the humoral theory, which held that illness resulted from imbalances among four bodily fluids: blood, phlegm, yellow bile, and black bile. Treatment revolved around restoring this balance through diet, bloodletting, or herbal remedies.

The miasma theory 🔗

By the Middle Ages and into the 18th century, the miasma theory became dominant. It proposed that diseases like cholera and plague were caused by “bad air” – foul-smelling vapours rising from rotting organic matter, swamps, or sewage. While the theory was scientifically incorrect, it did lead to important sanitation reforms. Cities improved waste management and drainage systems, inadvertently reducing the spread of waterborne and insect-borne diseases.

Germ theory: a turning point 🔗

The real breakthrough came in the 19th century with the germ theory of disease. Scientists like Louis Pasteur and Robert Koch demonstrated that specific microorganisms – bacteria, viruses, fungi, and parasites – cause specific diseases. Koch developed a set of criteria, known as Koch’s postulates, for proving that a particular microbe causes a particular disease. This shift from vague environmental explanations to precise biological causation transformed medicine, paving the way for vaccines, antibiotics, and modern infection control.

However, germ theory had its limitations. It could not explain why some people exposed to the same pathogen became severely ill while others remained symptom-free. Nor could it account for chronic, non-infectious diseases such as heart disease and cancer, which clearly had no single microbial cause. These gaps demanded broader, more nuanced frameworks.

The epidemiological triad: a foundational framework 🔗

One of the most widely taught models in public health is the epidemiological triad (also called the epidemiologic triangle). According to this model, disease occurs through the interaction of three core elements: the agent, the host, and the environment.

Agent 🔗

The agent is the factor whose presence (or sometimes absence) initiates the disease process. For infectious diseases, agents include bacteria, viruses, parasites, and fungi. Over time, the concept has expanded to include chemical agents (like industrial pollutants or tobacco smoke) and physical agents (like radiation or repetitive mechanical stress). Each agent has characteristics that affect its ability to cause illness, including its pathogenicity (ability to cause disease), virulence (severity of the disease it produces), and infectivity (ability to establish an infection).

Host 🔗

The host is the human (or animal) capable of developing the disease. Host susceptibility depends on several intrinsic factors: age, genetic makeup, nutritional status, immunological condition, and behavioural factors like hygiene, diet, and lifestyle choices. Two individuals exposed to the same pathogen may have vastly different outcomes depending on these host characteristics. For example, an immunocompromised person faces far greater risk from a common infection than someone with a healthy immune system.

Environment 🔗

The environment encompasses all external conditions that influence the agent and host. Physical environmental factors include climate, geography, and housing quality. Biological factors involve vectors such as mosquitoes and ticks that transmit disease. Socioeconomic factors – crowding, sanitation levels, access to healthcare – also play a critical role in whether disease transmission occurs.

The triad model provides a clear insight: disrupting any one of the three components can prevent disease. Vaccines strengthen the host, antibiotics target the agent, and sanitation improvements modify the environment. However, this model works best for infectious diseases with clearly identifiable agents and proves inadequate for chronic diseases like cardiovascular disease or cancer, which involve multiple contributing factors without a single necessary cause.

BEINGS model of disease causation 🔗

To address the limitations of simpler models, the BEINGS model offers a more comprehensive framework. This model identifies nine interacting categories of disease determinants, whose first letters form the acronym:

B – Biological factors: These include innate biological characteristics of the individual, as well as living agents (bacteria, viruses, parasites) that can cause disease.
E – Environmental factors: Physical, chemical, and biological aspects of the surroundings, from air quality to water supply to housing conditions.
I – Immunological factors: The body’s defence mechanisms and their effectiveness. Prior infections, vaccinations, and immune disorders all influence disease outcomes.
N – Nutritional factors: Malnutrition weakens the immune system and increases susceptibility, while adequate nutrition supports the body’s ability to resist and recover from disease.
G – Genetic factors: Hereditary traits that influence disease risk and resistance, from inherited conditions like sickle cell disease to genetic predispositions for diabetes or cancer.
S – Services, social, and spiritual factors: Access to healthcare services, social conditions like poverty and education, and even spiritual well-being all shape health outcomes.

The BEINGS model is particularly useful in analysing health challenges in developing countries, where multiple factors often interact simultaneously. A child suffering from diarrhoeal disease, for instance, may be affected by contaminated water (environmental), bacterial infection (biological), weakened immunity from malnutrition (immunological and nutritional), inherited susceptibility (genetic), and lack of access to a clinic (services). Understanding all these layers helps design more effective interventions.

Multifactorial model and web of causation 🔗

As chronic diseases like heart disease, diabetes, and cancer became leading causes of death globally, it became clear that single-cause models could not explain them. Unlike tuberculosis, which requires the presence of Mycobacterium tuberculosis, coronary heart disease involves a complex mix of excess fat intake, smoking, lack of exercise, obesity, stress, and genetic predisposition. No single factor is sufficient on its own.

The multifactorial model 🔗

The multifactorial causation model recognises that chronic diseases arise from the interaction of numerous contributing factors rather than a single cause. This approach shifted the focus of public health from searching for individual “magic bullets” to understanding patterns of risk and developing prevention strategies that address multiple factors simultaneously. For example, preventing cardiovascular disease involves promoting healthy diets, encouraging physical activity, discouraging tobacco use, managing stress, and ensuring access to medical care – all at once.

Web of causation 🔗

Building on this idea, MacMahon and Pugh introduced the web of causation in their 1960 textbook on epidemiological methods. This model visualises disease causation as a network of interconnected factors, where each strand represents a different risk factor and diseases emerge at points where multiple strands converge. The web remains a widely accepted framework for understanding the complex precursors of chronic disease.

A key strength of the web model is that it shows how factors can be both causes and effects, creating feedback loops. Poverty may limit access to nutritious food, leading to obesity, which increases the risk of diabetes, which in turn generates medical expenses that deepen poverty. This interconnected view helps identify leverage points – places where an intervention can break a chain of causation. Importantly, the web does not require that all causes be addressed simultaneously; sometimes removing a single critical link can be enough to prevent disease through that particular pathway.

Rothman’s causal pies 🔗

Another important model that complements the web of causation is Rothman’s Causal Pies, proposed in 1976. In this model, individual risk factors are represented as slices of a pie. When all slices fall into place, the pie is complete – and disease occurs. Each individual factor is called a component cause, and the complete pie represents a sufficient cause. A disease may have multiple sufficient causes, each made up of different combinations of component causes.

A necessary cause is a component that appears in every sufficient cause pie. For instance, HIV is a necessary cause of AIDS – without the virus, AIDS cannot develop. But HIV alone is not sufficient; other factors like immune status and co-infections influence disease progression. In contrast, smoking is a component cause of lung cancer but not a necessary cause, since lung cancer can develop in non-smokers through other pathways.

The practical value of this model is that public health action does not require identifying every component cause. Blocking any single component of a sufficient cause can prevent disease through that pathway. This is why smoking cessation programmes are effective even though smoking is not the only cause of lung cancer.

Natural history of disease 🔗

Understanding how a disease develops over time – without medical intervention – is crucial for designing prevention strategies. The natural history of disease describes this progression and is typically divided into two main phases.

Pre-pathogenesis phase 🔗

This is the period before the disease process begins in the body. The individual is exposed to risk factors, but the disease agent has not yet entered or begun to affect the host. During this susceptibility stage, primary prevention measures – such as vaccination, health education, and environmental sanitation – can be implemented to stop the disease from developing at all.

Pathogenesis phase 🔗

Once the disease agent enters the host, the pathogenesis phase begins. It progresses through several stages:

Subclinical (preclinical) stage: Pathological changes are occurring inside the body, but the individual has no obvious symptoms. In infectious diseases, this includes the incubation period. In non-infectious diseases, it represents a latency period that can last years or even decades. Many screening programmes target this stage because early intervention is often more effective than treatment after symptoms develop.
Clinical disease stage: Signs and symptoms appear, and diagnosis typically occurs. Severity depends on factors like the virulence of the agent, host immunity, and access to care.
Resolution stage: The disease concludes with one of three outcomes: recovery, disability, or death.

Understanding natural history helps healthcare systems decide when to screen, how to allocate resources, and how to educate patients. For example, cervical cancer develops slowly over many years, which supports the effectiveness of regular screening to detect precancerous changes early.

The iceberg concept of disease 🔗

One of the most powerful metaphors in epidemiology is the iceberg concept of disease. It illustrates a fundamental truth: the clinically visible cases of a disease represent only a fraction of the total disease burden in a population – just like the tip of an iceberg above water.

The visible tip 🔗

The visible portion includes hospitalised patients, those seeking medical care, and individuals with severe or obvious symptoms. These are the cases that get counted in official statistics and reported to public health authorities.

The hidden portion 🔗

Below the surface lies a much larger group: people with subclinical disease (pathological changes but no symptoms), undiagnosed conditions, mild infections that do not prompt a doctor visit, and asymptomatic carriers who can still transmit disease to others. This hidden burden often far exceeds the number of visible cases.

The COVID-19 pandemic provided a vivid example of the iceberg concept in action. A significant proportion of infected individuals were asymptomatic or had only mild symptoms, yet they were capable of transmitting the virus. This made containment exceptionally difficult and highlighted why relying solely on reported clinical cases gives an incomplete – and dangerously optimistic – picture of disease prevalence.

The size and shape of the iceberg vary depending on the disease, influenced by agent-host-environment dynamics and the natural history of the particular condition. For diseases like polio, the subclinical portion is enormous: the vast majority of infections produce no paralysis. For diseases like rabies, virtually all infections progress to clinical disease, making the iceberg nearly all visible.

The iceberg concept has direct implications for public health: it underscores the need for active surveillance, screening programmes, and community-level interventions rather than relying only on treating individuals who present with symptoms.

What do you think? How might understanding these different causation models change the way you evaluate conflicting health advice in everyday life? Can you identify a chronic health condition in your community and trace the web of interconnected factors – from genetics and diet to environment and healthcare access – that might contribute to its prevalence?

References

1. https://archive.cdc.gov/www_cdc_gov/csels/dsepd/ss1978/lesson1/section8.html
2. https://openstax.org/books/population-health/pages/12-3-epidemiological-approaches
3. https://courses.lumenlearning.com/suny-buffalo-environmentalhealth/chapter/components-of-the-triad/
4. https://www.nhp.gov.in/causation-of-diseases_mtl
5. https://disaster.shiksha/occupational-health-safety-management/theories-disease-causation-germ-theory-web/
6. https://pubmed.ncbi.nlm.nih.gov/7992123/