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Epidemiology: Science as a Tool to Inform One Health Policy

Yvette J. Johnson‐Walker1 and John B. Kaneene2

1 University of Illinois Urbana‐Champaign, Urbana, IL, USA

2 Michigan State University, East Lansing, MI, USA

1.1 Introduction

Epidemiology is the study of disease dynamics in populations. It seeks to understand patterns of disease as a means of identifying potential prevention and control measures. It has been described as “an interesting and unique example of cross‐fertilization between social and natural sciences” (Vineis, 2003). The basic principle of epidemiology is that disease is not a random event. Each individual in a population has a unique set of characteristics and exposures (risk factors) that determine his or her probability of disease. Clinical medicine is focused on the health of the individual while epidemiology and public health seek to apply assessment of risk factors at the community level. Understanding how those risk factors impact a community provides public health officials with the tools to develop policies and interventions for disease control and prevention in the community as a whole.

The One Health concept is coherent with the principles of epidemiology because risk factors for many diseases occur at the interface between humans, animals, and the environment. Failure to consider the interactions between them may result in public health policies that fail to effectively control disease and protect the environment. The One Health triad (Figure 1.1) of humans, animals, and the environment is analogous with the other triads that epidemiologists use to describe disease dynamics within a population:

• The host, agent, environment triad (Figure 1.2) is used to describe the interplay between these three key components of infectious disease transmission. Changes in any of these components alters the probability of disease.
• The three states of infectious disease status are illustrated by the susceptible, infected, removed (SIR) triad (Figure 1.3).
• Outbreaks of disease are characterized in terms of person or animal, place, and time as the first step of identifying the population at risk.
• Risk factors for disease causation are categorized as: necessary, sufficient, and component causes (Figure 1.4).

Figure 1.1 The One Health triad.

Source: Thompson, 2013. Reproduced with permission of Elsevier.

Figure 1.2 The “epidemiologic triad” of infectious disease summarizes the factors that influence an infection, and the measures you might take to combat the infection.

Source: Used with permission from Ian McDowell (http://www.med.uottawa.ca/SIM/data/Pub_Infectious_e.htm#epi_triad).

Figure 1.3 Infection modeling: the SIR model. Susceptible nodes – have not been infected yet and are therefore available for infection. They do not infect other nodes. Infectious nodes – have been infected and infect other nodes with a certain probability. Removed (recovered) nodes – have gone through an infectious period and cannot take part in further infection (neither actively nor passively).

Source: Used with permission from Michael Jaros (http://mj1.at/articles/infection‐modelling‐the‐sir‐model/).

Figure 1.4 Necessary, sufficient, and component causes. The individual factors are called component causes. The complete pie (or causal pathway) is called a sufficient cause. A disease may have more than one sufficient cause. A component that appears in every pie or pathway is called a necessary cause, because without it, disease does not occur.

Source: Rothman, 1976. Reproduced with permission of Oxford University Press.

The goal of public health policy is to prevent transmission of disease agents to the susceptible segment of the population by controlling and treating disease among the infected and increasing the segment of the population that is removed (recovered or resistant). Identification and isolation of cases, quarantine of the exposed, and vaccination of the susceptible are the primary tools employed by public health practitioners for infectious disease control. Development of effective programs to accomplish these goals requires an understanding of the:

1. Causes of disease (etiologic agent, pathophysiology, and risk factors.
2. Impact of the disease on the population (number of cases, ease of transmission, economic and social impact).
3. Natural course of the disease (reservoirs for the agents of disease, means of introduction of the agent into the population, period of infectivity, severity of disability, length of immunity, and potential for long‐term sequelae) (Figure 1.5).

Figure 1.5 Natural history of disease timeline.

Source: CDC, 1992.

The goals of this chapter are to elucidate how epidemiology can 1) provide a tool for understanding the causes, impacts, and course of disease in human and animal populations within various ecosystems, and 2) form the basis for evidence‐based health and environmental policy development.

1.2 Enhancing Our Understanding of Health and Disease

1.2.1 Causes of Disease

Epidemiology is unique among biomedical investigative approaches because of the observational nature of many of the study designs. Unlike laboratory studies, the epidemiologist often studies a naturally occurring disease within a free‐living population in which study subjects are not assigned to intervention groups (except in the case of clinical trials). Individuals may have a variety of independent exposures during the study period. Whether studying human or animal populations, the epidemiologist seeks to identify exposures that are associated with the probability of disease using statistical analysis of data from carefully documented exposures and outcomes. However, even if a statistically significant association between an exposure and disease outcome has been identified, that does not necessarily mean that a cause and effect relationship has been established. Much more rigorous standards have been set for establishing a causal relationship between a risk factor and the probability of disease.

1.2.1.1 Deterministic Models of Disease

Criteria for establishing causation for infectious disease have been described since the nineteenth century. Research by Robert Koch, Friedrich Loeffler, and Jakob Henle resulted in the Koch–Henle postulates published in 1882 (Sakula, 1983; Gradmann, 2014) (Figure 1.6). While this approach is useful when seeking to identify the etiologic agent responsible for an infectious disease, it has many limitations. The simplistic approach of a deterministic model for establishing disease causation is insufficient for identifying risk factors for chronic noninfectious diseases (such as type II diabetes) or even infectious diseases with a multifactorial etiology (such as new variant Creutzfeldt–Jakob disease, or CJD). In more recent years more complex models have been used to establish a causal relationship between a putative risk factor and disease.

Figure 1.6 The steps for confirming that a pathogen is the cause of a particular disease using Koch’s postulates.

1.2.1.2 Hill’s Causal Criteria

Austin Bradford Hill published “The environment and disease: association or causation?” in 1965 (Hill, 1965). The manuscript describes nine criteria necessary for establishing a causal relationship between a risk factor and a disease:

1. Strength of association: the greater the magnitude of the association between the risk factor and the outcome, the more likely the relationship is to be causal.
2. Temporality: the risk factor must precede the onset of the disease.
3. Consistency: the same association should be observed in multiple studies with different populations.
4. Theoretical plausibility: the association should be biologically plausible and consistent with the pathophysiology of the disease.
5. Coherence: the association should be consistent with what is known about the disease.
6. Specificity in the causes: a risk factor should be associated with a single disease or outcome.
7. Dose‐response relationship: as the dose of the risk factor is increased the probability and severity of the disease should increase in a linear fashion.
8. Experimental evidence: data from in vitro studies and animal models should support the causal association between the risk factor and the disease.
9. Analogy: similar causal relationships should be known.

The nature of these criteria makes it impossible for a single observational study to establish a causal relationship between an exposure and a disease outcome. The criterion of consistency requires that multiple studies, in different populations, show the same association. The criterion of temporality also requires that the association be demonstrated in prospective studies. Prospective study designs monitor the study population prior to the onset of disease and follow their...