2. Definition
Cross-sectional study is a type of observational study that investigates data
collected from a population or a representative subset, at a specific point in
time. It usually used in the area of social science, biology and medical
research.
Cross-sectional study is also called as a survey study (survey), or
prevalence study.
3. Cross-sectional study data characteristics
Cross-sectional study data is a type of data collected by observing multiple
subjects (such as individuals, firms, facilities, countries, or regions) at the one point
or period of time that give us the ability to get a ”cross section of a study
population” as it is called in statistics and econometrics.
In the cross-sectional study all data is collected at the same time by looking at
people who are similar in other characteristics, but differ on one key factor of
interest such as age, income levels, or geographic location.
4. Cross-sectional study /survey is used when we want:
To understand a prevalence of something.
To look for information on a general attitudes towards a number of things in
society (e.g. drinking, smoking) and an engagement in a number of behaviors (
e.g. type of eating, visit to the doctor, healthy life style).
To explore possible links between variables ( e.g. poor diet and being
overweight; or, the amount individuals smokes and number of times an individual
is absent from the work).
Use it in the areas of medical practice as an instrument to:
- understand a knowledge and an attitudes of persons about some issues
- needs assessment
- evaluation of content of practice and outcomes
5. Purposes of cross-sectional study
• Cross-sectional surveys are usually used for population surveys and for
estimating the prevalence of disease in clinical samples.
• A cross-sectional study measures both the exposure and the outcome of
interest at the same point in time.
• Despite this is a one-time measurement of exposure and outcome, it is very
difficult to derive causal relationships from cross-sectional study. We can only
estimate the odds ratios to study the association between exposure and the
outcomes.
• Cross-sectional study may be also useful for designing the cohort study, since this
type of design will give us information about the prevalence of exposures or
outcomes. Thus, it may be conducted either before planning a cohort study or as
a baseline in a cohort study.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4885177/
6. Types of cross-sectional studies
Cross-sectional studies usually use a quantitative research design to assess the
burden of a particular disease in a defined population. For example, a random
sample of schools across particular town may be used to assess the prevalence of
asthma among 12-15 year old children.
Cross-sectional study - is mostly descriptive study because it describes the cross
section of population in which both the disease and potentially related risk factors
are measured at a specific point in time for a defined population.
Some researchers may use an analytical method in cross-sectional studies to
investigate the association between a potential risk factor and a health outcome, but
is limited in its ability to draw valid conclusions, because in a cross-sectional study
the risk factors and outcome are measured simultaneously, therefore it may be
difficult to determine whether the exposure proceeded or followed the disease.
Both, descriptive and analytical approach in cross-sectional study mostly used in
census.
7. Type of cross-sectional study - population census
• Probably one of the most popular examples of cross-sectional studies, is
population census (demographic study), that is systematically carried out by
many countries in order to identify characteristics of their populations at a given
time, analyze their evolution over time, and to establish some relationships
between these features that deserve to be analyzed.
• A census covers the collection of data from the entire population (the universe)
that is to be evaluated, that’s why it involves a lot of time and resources, and,
therefore, its routine use practically unfeasible in most scientific research.
• As a consequence, to reduce the cost and time of conducting the survey it is
often necessary to use samples (from the universe) that, based on statistical
analyzes, produce results and estimates capable of producing generalizable
conclusions, even with some limitations.
8. Cross – sectional study/survey design
While the cohort studies or case-control studies look for information relating to a
particular health related factors (risk factors or to a particular disease), cross -
sectional studies looking for lot more information about health behavior,
attitudes for local health services and their relationships for all sorts of topics.
Cross-sectional studies / survey are designed in a way that provide the researcher
with information about a group/s of people at a specific time, the so-called
“frozen” in time. This is as opposed to cohort studies and case-control studies
which last over time.
If researcher wants to see if peoples’ experiences of health and illness have
changed over time, then it might be best to use so called a longitudinal survey
design.
11. Common characteristics of cross-sectional studies
The study takes place at a single point in time.
It allows researchers to look at numerous characteristics at once (age, gender, etc.)
It's often used to look at the prevailing characteristics in a given population.
It can provide information about what is happening in a current population, may
also be described as censuses.
It does not involve manipulating variables.
While this type of study cannot demonstrate cause-and-effect, but it can
provide a quick look at correlations that sometimes may exist at a particular point.
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12. Relevance of cross-sectional studies
• Cross-sectional studies can be thought of as providing a snapshot of the
frequency of a disease or other health related characteristics (e.g. exposure
variables) in a population at a given point in time.
• A cross-sectional study examines the relationship between disease (or other
health related state) and other variables of interest as they exist in a defined
population at a single point in time or over a short period of time (e.g. calendar
year).
• Cross-sectional studies are used to assess the burden of disease or health needs
of a population and are particularly useful in informing the planning and
allocation of health resources.
https://www.healthknowledge.org.uk/
13. Steps in conducting cross-sectional study
State the criteria for study clearly
Define factors (co-variables) to be measured
Identify the reference population – representative sample
Identify inclusion / exclusion criteria
Conduct study
Provide data analysis
14. Study population samples
The study populations can be:
• a population of a city, a state or a country as a whole (universe),
• or, as the population of certain subgroups (for example the female or child population),
• or, the population with a certain health problem (such as the population of diabetics or
hypertensions),
• or, the population with exposure to risks (such as smokers, sedentary and so on).
Researchers must define which conditions/ characteristics and which population is
to be studied, that is, the population from which a sample will be selected.
Whenever it is not possible to obtain a representative sample, the researcher will
be analyzing data of what is called the hypothetical population.
15. Identify the reference population
• Choosing a representative sample
A cross-sectional study should be representative of the population if generalizations
from the findings are to have any validity.
For example, a study of the prevalence of diabetes among women aged 40-60 years in
“town A” should comprise a random sample of all women aged 40-60 years in that
town.
• Sample size
The sample size should be sufficiently large enough to estimate the prevalence of the
conditions of interest with adequate precision. Sample size calculations can be carried
out using sample size tables or statistical packages (such as EpiInfo).
16. Identify inclusion / exclusion criteria – selection the study
samples
• Unlike case–control studies where participants are selected based on outcome
status, or cohort studies where participants are selected based on exposure
status, cross-sectional study participants are selected based on the inclusion
and exclusion criteria established for the study.
• Inclusion criteria are characteristics that the prospective subjects must have if
they are to be included in the study. The inclusion criteria identify the study
population in a consistent, reliable, uniform and objective manner.
• Exclusion criteria are those characteristics that disqualify prospective subjects
from inclusion in the study. The exclusion criteria include factors or characteristics
that make the recruited population ineligible for the study, because these
factors may be confounders the outcome parameters.
18. Conduct questionnaire – data collection tools
• As the instruments to collect survey data, questionnaires are used.
• Questionnaire usually provides a set of questions from which a person chooses
one of more options in answer to a particular questions or statements, or asks
the respondent to provide some written information about a particular area or
number of particular areas.
• Types of questions and possible responses can vary but the core of each
questionnaire and survey is the need to collect information that can be
quantified.
• Questionnaire are wildly used when:
• The study sample is large
• Time is limited
• Resources are limited
• Needs to keep privacy of participants
19. Designing questionnaires
• General recommendations suggest taking into account
the following:
• Questions format
• Closed / open questions
• Clarity of questions
• Types of question to avoid
• Response format
(see also the topic “Data collection and analysis”)
20. Data analysis in cross-sectional studies
• In a cross-sectional study all factors (exposure, outcome) are measured
simultaneously. The main outcome measure obtained from a cross-sectional study
is prevalence, that is:
number of cases in a defined population at one point of time
Prevalence = ----------------------------------------------------------------------------------
number of persons in a defined population at same point of time
In cross-sectional studies, the odds ratio may be used to assess the strength of an
association between a risk factor and health outcome of interest, provided that the
current exposure accurately reflects the past exposure.
21. Measurement in cross sectional study
Case # 1. Cross-sectional study designs may be used for estimating the prevalence
in population-based survey
Example: We are interested to know the prevalence of vitiligo in a village N.
• We design a population-based survey to assess the prevalence of this condition.
We go to all the houses that were supposed to be included in the study and
examine the population. The total sample surveyed is 5686. Of these, we found
that 98 individuals have vitiligo.
• Thus, the prevalence of vitiligo in this community is: Prevalence = 98/5686 or
17.23/1000 population
22. Cross-sectional data analysis
For this analysis it is possible to divide the subjects of the sample, according to
the risk factor and outcome (disease), into four distinct groups:
1. those who have the risk factor and have the outcome
2. those who have the risk factor and do not have the outcome
3. those who do not have the risk factor and have the outcome
4. those who do not have the risk factor and do not have the outcome
23.
24. Strengths of cross-sectional studies
Relatively quick and easy to conduct (no long periods of follow-up).
Data on all variables is only collected once and there is no follow up necessary.
Able to measure prevalence for all factors under investigation.
Multiple outcomes and exposures can be studied.
The prevalence of disease or other health related characteristics are important in
public health for assessing the burden of disease in a specified population and in
planning and allocating health resources.
Good for descriptive analyses and for generating hypotheses.
25. Weaknesses of cross-sectional studies
Difficult to determine sequence - whether the outcome followed exposure in
time or exposure resulted from the outcome.
Not suitable for studying rare diseases or diseases with a short duration.
As cross-sectional studies measure prevalent rather than incident cases, the data
will always reflect determinants of survival as well as etiology.
Unable to measure incidence.
Associations identified may be difficult to interpret.
Susceptible to bias due to low response and misclassification due to recall bias.
Tend to identify prevalent cases of long diseases, since people who changed their
status quickly are less likely to be identified.
27. References
1. J. Maltby et al. Research methods for nursing and healthcare. 4th edition.
2. J.W. Cresswell. Research design. 4th edition. Chapters 6-9.
3. Juliana Zangirolami-Raimundo; Jorge de Oliveira Echeimberg; Claudio Leone. Research
methodology topics: Cross-sectional studies. J. Hum. Growth Dev. vol.28 no.3 São
Paulo set./dez. 2018 http://pepsic.bvsalud.org/scielo.php?script=sci_arttext&pid=S0104-
12822018000300017
