8 Cross-sectional study
8.1 Design and why do Cross-sectional study?
a Design
In a cross-sectional study, we measure the frequency of a particular exposure(s) and / or outcome(s) in a defined population at a particular point in time. Sometimes it is called prevalence study.
In a descriptive cross-sectional study, we simply describe the frequency of the exposure(s) or outcome(s) in a defined population.
In an analytic cross-sectional study, we simultaneously collect information on both the outcome of interest potential risk factor(s) in a defined population. We then compare the prevalence of the outcome in the people exposed to each risk factor with the prevalence in those not exposed.
b Why do Cross-sectional study?
Descriptive study
as the study measures the prevalence, it is useful for health planners who need to know the burden of specific conditions in order to plan preventive and curative services.
some cross-sectional studies also collect data on the utilisation of preventive and curative services. it’s useful for predicting health care needs and establishing health-care priorities.
Some studies collect information about the knowledge, attitudes and practices of a population in relation to a particular health-related outcome. it’s useful for the development of population-level health education campaigns.
Cross-sectional studies are not suitable for describing the frequency of rare exposures or outcomes.
Analytical study
as the study measures the prevalence at a particular time point, it does not provide strong evidence about the causality. It can be difficult to establish the time sequence of events: the exposure may be a consequence rather than a cause of the outcome (reverse causality)
We cannot use cross-sectional study to test a hypothesis. Instead, we use it to generate hypothesis.
8.2 Main steps in conducting a Cross-sectional study
Step 1: Defining the study question
Step 2: Defining the target population and selecting the study population
Refer to Measures of Occurrence section for definition of different concepts of population. Basically, the target population is the group to which we can generalise our study results, while the study population consists of the individuals we intend to include in our study. If the target population is small, we can study the entire population. However, in practice, the study population is usually a sample of the target population.
It is important to select the sample as representative of the target population, otherwise we will introduce selection bias in our study. The best way to ensure this is random sampling: Simple random sampling, Systematic sampling, Stratified sampling, Multi-stage sampling.
Most of the time, not all individuals of our target population will participate in our study even though we have tried our best. We should collect as much as possible information about those who do not participate. If there is systematic different characteristics between those who participate and those who do not, there is selection bias in our study. And, We should report as much information as possible about the non-participant.
Step 3: Collecting data
Have a clear case and exposure definitions Measure of Occurrence before collecting any data. We then collect data on both outcomes and exposures simultaneously.
Methods used for collecting data could be: personal interviews, questionnaires, medical records, physical examinations, diagnostic tests, or any combination of these. The data should be collected in standardised way.
Step 4: Analysing data
In Descriptive study: We quantify the prevalence and often report this in different gender and age groups.
In Analytical study: We quantify the association between exposure and outcome of interest using Prevalence ratio.
Step 5: Interpreting results
Bias in cross-sectional studies
Selection bias may occur as we cannot have 100% individuals participate in our study
Information bias
Recall bias: if the individuals with the outcome are more of less likely to recall having been exposed to the risk factor risk factors. We can minimise this bias by trying to use objective data, not telling participants about the association under the study.
Observer bias: If the researchers is more or less likely to decide that the outcome is present in individuals who have the exposure under study. We can minimise this bias by having a strict definition of outcome, using standardised methods of data collection or ensure the researcher who measure the outcome is blinded to the exposure status of the participants.
Confounding in cross-sectional studies
We must be aware of all potential confounders and collect data on these confounders at the start of the study. In analysing step, we should check for the evidence of confounding using the 3 necessary criteria and assess how the confounders distort the association.
Cross-sectional study measure the prevalence which is influenced by many factors: severity of cases, access to curative treatment, survival. It’s difficult to intepret the reasons for the differing prevalences in different populations.
Data on exposure and outcome are collected at the same time. It can be difficult to tell whether outcome really occur after or before exposure. if it occurs before the exposure we call reverse causality. We can only be sure that the exposure occurred before the outcome if it is fixed characteristic of the individual(e.g. gender, age, blood group….).
8.3 Strength and Weakness
Strengths
Cross-sectional studies are relatively easy and economical to conduct
Cross-sectional studies provide important information on the distribution and burden of exposures and outcomes. This is extremely valuable for health-service planning.
Cross-sectional studies can be used as the first step in the study of a possible exposure-outcome relationship.
Weaknesses
Cross-sectional studies measure prevalence rather than incident cases. Because of this, they are of limited value for investigating aetiological relationships. Any association identified in a cross- sectional study is a measure of the effect of developing the outcome and staying in the population with the outcome.
It can be difficult to establish the time-sequence of events in a cross-sectional study. The exposure may have occurred as a result of the outcome (reverse causality).