A cross-sectional study is one that takes place at a single point in time.

8.2.3 Cross-sectional studies

Cross-sectional studies measure the prevalence of conditions or characteristics of people in a population at a point in time or over a short period. Although they are essentially descriptive studies, their results can often suggest causative or risk factors associated with particular illness or behavior; for instance, the causal relationship between cataracts and vitamin status was originally investigated through a cross-sectional study—The Blue Mountains Eye Study [70]. They may also be used to ascertain the prevalence of a health-related behavior, such as the wearing of seat belts or participation in exercise. In cross-sectional studies, it is not always necessary to investigate the whole population: a sample is usually sufficient, provided that the individuals in the sample are representative of the total group under consideration. Cross-sectional studies are useful in planning public health interventions.

A population or group can be studied in a variety of ways: by questionnaire, by taking measurements (such as blood pressure), by analyzing blood specimens (e.g., for blood cholesterol levels), or by examining health care records [71].

While cross-sectional studies can provide information on things like the prevalence of a particular disease (how common it is), they cannot tell us anything about the cause of a disease or what the best treatment might be [72]. They are rarely used in studies of cancer causation or prevention.

Cross-Sectional Study

Cross-sectional studies measure the cause (exposure) and the effect (disease) at the same point in time. They compare the rates of diseases or symptoms of an exposed group with an unexposed group. Strictly speaking, the exposure information is ascertained simultaneously with the disease information. In practice, such studies are usually more meaningful from an etiological or causal point of view if the exposure assessment reflects past exposures. Current information is often all that is available but may still be meaningful because of the correlation between current exposure and relevant past exposure.

Cross-sectional studies are widely used to study the health of groups of workers who are exposed to possible hazards but do not undergo regular surveillance. They are particularly suited to the study of subclinical parameters, such as blood biochemistry and hematological values. Cross-sectional studies are also relatively straightforward to conduct in comparison with prospective cohort studies and are generally simpler to interpret.

3.4.2.4 The Cross-Sectional Study

The cross-sectional study is an observational study that assesses exposure and the outcome at one specific point in time in a sample population. There is no prospective or retrospective follow-up. Both the RR and the OR can be calculated to describe the association between the exposure and the outcome. The incidence and prevalence, important descriptive measures, can also be calculated from a cross-sectional study.

The cross-sectional study cannot be used to infer causality because a temporal sequence cannot be established. Nevertheless, this type of study is used to generate descriptive statistics regarding the disease/outcome burden in a population, or to determine background exposure rates. All of which can be very useful, especially during the premarket phase of a product's life cycle. Fig. 3.3 below shows a schematic representation of a cross-sectional study.

5.4.1.1 Cross-sectional studies

Cross-sectional studies have a sample of individuals collected from a population of interest. Individuals within the sample are determined to be diseased or not. These studies examine the association between disease and risk factors. Calculations of disease prevalence estimations from cross-sectional studies can occur. Thorburn (1996) concluded that farmer experience, number of employees, water flow, use of prophylactic treatments, and drug treatment were associated with disease outbreaks in trout production facilities in Canada. Disease outbreaks of Ostreid herpesvirus type-1 in oysters are affected by increasing turbidity, how concentrated suspended particulate matter is, and land-based inputs (Pernet et al., 2018). Piamsomboon et al. (2015) found that white spot disease risks were associated with Taiwanese farms sharing inlet water, farms culturing shrimp year-round, and when one farmer owns multiple farms.

Cross-sectional Studies

Cross-sectional studies involve comparing cognitive performance between a group of postmenopausal women who use estrogen and a group of estrogen non-users who were matched for relevant control variables with the estrogen users. The majority of these studies found that estrogen users performed significantly better than non-users on tests of verbal fluency,79–81 verbal memory,82–84 and verbal71,85 and spatial86 working memory. Although these findings are largely consistent with those of the RCTs, the quality of the evidence is less robust since methodological weaknesses are, of course, inherent in cross-sectional designs. It should be noted, though, that the majority of the cross-sectional studies discussed above attempted to control statistically for participant characteristics that are known to independently influence cognitive functioning in older people, such as age, level of education and socioeconomic status (see Sherwin and Henry79 for a more comprehensive review).

Cross-sectional studies

Cross-sectional studies or surveys measure both the exposure and outcome in a sample of the population at a point in time. Ideally, the sample should be randomly selected from the population. Here, a matter of concern is the proportion of selected individuals who refuse to participate, since they are almost certainly dissimilar in some way from those who consent. The larger the refusal rate, the greater the likelihood of response bias within the sample. Analysis of such studies should always report the number of eligible individuals who were initially selected and approached and what proportion of them enrolled in the study.

Surveys of representative samples capture the prevalence of disease in the population being studied. It is also possible to test for associations of prevalent disease with potential risk factors, but not possible to know whether the exposure preceded the effect. Since temporality of association is a strong criterion for causality, cross-sectional studies cannot prove causality but help to generate causal hypotheses. Cross-sectional surveys of representative samples are useful in the assessment of healthcare needs of the population and are often used by countries and regions for this purpose. Repeated surveys can provide important information regarding health trends.

Does a cross

Is a cross

What is a cross