A cross sectional study provides information about the burden of disease within a population and the relationships between various exposures and outcomes. These do not use experimental methodology but are extremely beneficial scientific studies, especially for identifying risk factors, studying beliefs and attitudes about a given subject, and learning more about factors related to a specific outcome.
A cross sectional study is the scientific version of a still life image. Only, rather than capturing expressions and split second glimpses of a visual scene, it captures the equivalent of a snapshot of health status among a specific group of people, typically referred to as a population. These studies collect data about the population at a single moment in time, providing evidence about the prevalence of an exposure and an outcome or the frequency of an exposure and outcome. In other words, they tell us what is happening right now.
For example, a cross sectional study may evaluate how many individuals in a town currently smoke and also have lung cancer. The researchers would analyze those data to compare how common lung cancer is among smokers as compared to non-smokers. But because it captures a moment in time, not a progression over time, it does not establish causation. In this example, it does not capture former smokers who have quit. It also does not capture whether the smoking habit began before or after the diagnosis of lung cancer. Like a snapshot, it does not identify what led to the event in the image. Additionally, it does not reflect what happens after the picture. It reflects health status in a specific moment in time.
These studies do not use control groups or randomization. Rather they use a single sample group which accurately reflects a specific population of interest. Both descriptive and analytical cross sectional studies are conducted. Descriptive cross sectional studies only gather information about the outcome or behavior whereas analytical cross sectional studies may gather information about both an outcome and an exposure at the same time. The key distinction is that information is all gathered at once, like a snapshot.
Purpose: These studies collect data about the population at a single moment in time
Timing: The key distinction is that information is all gathered at once, like a snapshot.
Population: These studies do not use control groups or randomization
Effect: Identify patterns and relationships. Causation can be ruled out but not confirmed.
These studies are useful for public health planning as they describe the burden of exposures and disease within a population. If a program planner wanted to know more about the prevalence of diabetes in a community, a cross sectional study would be the right tool for that research question. They are also useful for generating a hypothesis related to a relationship between exposures and outcomes. In the above example, if the cross sectional study found that smokers were more likely to also have lung cancer, that finding may be used to generate a hypothesis about risk factors related to lung cancer.
In natural health, these studies provide information about the commonality of usage, about attitudes and beliefs prevalent among individuals who use natural health, and even about the risk factors unique to individuals who use natural health. We can learn how prevalent essential oil usage is within a given population and see if there are any relationships related to essential oil usage. Perhaps individuals who use essential oils are also more likely to eat healthier diets. Or maybe they exercise more frequently. This does not, however, mean that using essential oils leads to an increase in exercise or healthier diets.
The ability of cross sectional studies to confirm or rule out the presence of relationships makes them an ideal tool for studying the potential for harmful effects from various exposures. Because a relationship is required to establish causation, the absence of any relationship can inform researchers that some other factor is causing a particular health outcome. This is the process used for one of the studies the FHRF is conducting on tea tree and lavender essential oils as possible endocrine disruptors.
These studies are not useful for studying factors which are extremely rare (such as a rare form of cancer or a rare health behavior) because they are unlikely to include enough individuals with that specific factor or outcome to generate any useful information. Additionally, they are not designed to confirm causation. Because they do not establish that an exposure occurred before the outcome, cross sectional studies cannot determine which came first. Attempting to use a cross sectional study as evidence of causation would be a misinterpretation of the research design and study. They can, however, rule out causation.
Cross sectional studies provide information about existing exposure or outcomes (prevalence) and are not designed to tell us anything about incidence–or new cases/outcomes. So if a cross sectional study were evaluating how many individuals in a community used essential oils, it would only be able to tell how many currently use essential oils and whether or not there were any relationships between essential oil usage and a factor of interest. Such a study would not tell us if these individuals have been using essential oils for decades or if they only began yesterday.
Cross sectional studies are the first step to identifying and confirming a relationship between two factors. They identify and describe problems that need to be addressed by health professionals. They also help to monitor the overall health of a specific population. Cross sectional studies which evaluate the diabetes rate of a specific town can be compared to previous cross sectional studies to determine whether rates are increasing, decreasing, or stable.
In integrative health, they tell us a lot about the needs and values of individuals we will assist. They also tell us what specific areas we should be studying to ensure that we continue to meet the needs of our clients. They can tell us about correlations that exist between individuals who use herbs or essential oils and various negative outcomes. They pinpoint areas of precaution and provide insight into where research is headed in terms of discovering more about the risk factors and causes of conditions which our clients face.
• Do general practitioners in Mexico have sufficient knowledge about the latest findings related to Helicobacter pylori infections? The answer to this one is no, according to Cano-Contreras, et al, 2017.
• Are individuals in Nigeria who have diabetes more likely than non-diabetic patients to use complimentary and alternative medicine? Yes, according to Ogbera, et al, 2010.
• Given that previous studies have found certain herbal supplements to be contaminated with lead, is there a relationship between the use of dietary supplements and increased levels of lead in the blood? Yes, women who use herbal supplements do have higher levels of lead in their blood, according to Buettner, et al, 2009.
Cross sectional studies are useful tools in the overall research methods toolkit and knowing how to both recognize a cross sectional study and utilize a cross sectional study will empower you to both evaluate and interpret these snapshot studies when looking for more information about a specific moment in time. Remember that they are not intended to produce information related to causation.