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The Canary Database
Center for One Health Research
University of Washington

Study methodologies: Cross sectional

The Canary database curators determine, for each included study, the type of study methodology employed by the researchers (using this classification protocol). The possible categories are:

Fox has outlined criteria for objectively evaluating the relationship between an environmental hazard and an observed health effect in an observational study of animals (Fox 1991). These include probability, time order, strength of association, specificity, and consistency on replication, predictive performance, and coherence. The choice of study design can have a major effect on the ability of a study to fulfill such criteria.

Our preliminary review of the animal sentinel literature has found that some potentially useful study designs, such as case-control and cohort, are under-utilized in animal sentinel research.

Cross-sectional Studies

In a cross-sectional study, both outcomes and exposures are assessed on the individual level, but at a moment in time, without either forward or backward timing. Cross-sectional studies may be almost as simple and quick to perform as aggregate studies. In an elegant example of this, investigators used paired samples of eggs to correlate dioxin exposure levels to brain development in wild blue herons (Henshel, Martin et al. 1995). Many of the infectious disease studies in the Canary database measure antibodies to infection, and were therefore classified as cross-sectional since the test determined, on the individual level, both the occurrence of exposure to an infectious agent as well as evidence of effect (infection).

Since information on both exposure and effect is available for each subject, cross-sectional data can be analyzed either in terms of outcome or exposure. When they compare groups on the basis of exposure, they resemble a cohort analysis. When they sample groups for analysis based on outcome, the analysis is similar to that of a case-control study. Analyzing on the basis of outcome involves taking a group of affected individuals affected by a particular health outcome, and comparing them with a group that does not have the outcome. Levels of contaminants or evidence of infectious exposure can then be compared between groups. Conversely, analyzing a cross-sectional study in terms of exposure involves comparing the degree of health effects in a group that has a certain exposure, such as a certain elevated level of contaminants, with another group that has a lower level of contaminants.

In human epidemiology, the decision whether to sample and analyze based on outcome or exposure is generally made on the basis of whichever is rarer (Kramer and Boivin 1987). If an outcome is fairly rare, such as a limb deformity in a frog population, it makes sense to attempt to sample on the basis of outcome, since a sufficient number of cases could be assembled more economically and compared with a number of unaffected individuals than if an entire population had to be sampled. An advantage of this approach is that for a particular outcome, a number of different etiologies can be explored. If on the other hand, an exposure is fairly rare, it makes sense to concentrate on the subjects that have that exposure (as in a cohort design), and look for health effects in this population compared to an equivalent number of individuals that do not have the exposure.

Animal sentinel studies often sample on the basis of exposure rather than outcome. For toxin studies, this often involves comparing individuals in a "polluted area" to individuals in a "reference" (less polluted) area and measuring differences in outcomes between the two groups. For example, a study compared lung histopathology in ring-billed gulls living near a point-source of air pollution with that of gulls living in a geographically removed area with better air quality (Yauk, Smits et al. 2001). Similarly, infectious disease studies often compare rates of infection in animals living in one geographic area to those in another area to determine risk factors for infection, as when raccoons in two distinct areas were compared for rates of leptospirosis (Mitchell, Hungeford et al. 1999).

This assumes that there is little diversity of exposure among individuals in the "polluted site", and that it is necessary to choose "unexposed" individuals from a separate population. In studies of animal populations, where natural selection may be at work even in populations separated by small distances, such an approach runs the risk of "selection bias", i.e. making comparisons between two groups of individuals that are not really comparable.

For example, in their study of fish exposed to sewage and other water pollutants, Karels et al found differences in the population structure between fish who lived near sewage outflow compared to those individuals living in a geographically removed location (Karels, Markkula et al. 2001). Such selection pressures can cause genetic shifts, perhaps toward greater adaptation, for a population exposed to hazards, and make comparisons to another population less valid. For example, deer mice captured from areas of high air pollution level have been found to be more resistant to the effects of ozone compared with deer mice from less polluted areas, indicating that a process of genetic adaptation on a population level had taken place (Dickerson, Hooper et al. 1994). Similarly, it is possible that different animal populations could have different levels of immune function that could affect infection rates.

A key weakness of both ecologic and cross-sectional studies of toxins and health outcomes is that they cannot determine facts about "time order" of exposure and effect, in other words whether one preceded the other. This is less of a problem for infectious disease studies where both the outcome and the exposure are for the same infectious disease. Another disadvantage of cross-sectional studies is that if a toxin or infection has a fatal effect on animals, a cross-sectional study will tend to under-select individuals who have been exposed. This is known as "late-look bias." Alternatively, the cross-sectional study may over represent exposures that have sub-lethal effects (Neyman bias). This can be a problem in studies of asymptomatic animal reservoirs of infection. It is possible that the only survivors in an infected population are those individuals who experienced a mild infection, thereby again introducing selection bias into the study. In this way, a cross-sectional study could miss a transgenerational effect that initially caused widespread mortality of more susceptible individuals.

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